Telecommunications Science ›› 2022, Vol. 38 ›› Issue (12): 65-77.doi: 10.11959/j.issn.1000-0801.2022238

• Research and Development • Previous Articles     Next Articles

Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior

Huahua CHEN, Zhe CHEN   

  1. School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
  • Revised:2022-08-11 Online:2022-12-20 Published:2022-12-01
  • Supported by:
    The “Leading Goose” Technologies Research and Development Program of Zhejiang Province(2022C03065)


Anomaly detection remains to be an essential and extensive research branch in data mining due to its widespread use in a wide range of applications.It helps researchers to obtain vital information and make better decisions about data by detecting abnormal data.Considering that sparse coding can get more powerful features and improve the performance of other tasks, an anomaly detection model based on sparse variational autoencoder was proposed.Firstly, the discrete mixed modelspike and slab distribution was used as the prior of variational autoencoder, simulated the sparsity of the space where the hidden variables were located, and obtained the sparse representation of data characteristics.Secondly, combined with the deep support vector network, the feature space was compressed, and the optimal hypersphere was found to discriminate normal data and abnormal data.And then, the abnormal fraction of the data was measured by the Euclidean distance from the data feature to the center of the hypersphere, and then the abnormal detection was carried out.Finally, the algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the experimental results show that the proposed algorithm achieves better effects than the state-of-the-art methods.

Key words: anomaly detection, variational autoencoder, spike and slab distribution, deep support vector network

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